Background: Cancer-associated cachexia (CAC) is a complex multifactorial syndrome. Various antipsychotics have been associated with weight gain, especially olanzapine (OLZ) which is suggested to have a role in the management of CAC. It remains unclear whether OLZ has an ameliorating effect on cancer patients’ anorexia, even when those patients exhibit no nausea or vomiting. Given the potential effect of OLZ on CAC and the mixed findings regarding this subject, the current trial aims at evaluating the efficacy of OLZ as a monotherapy in incurable cancer patients with cancer cachexia-associated anorexia.

Trial design: This is a single institution double-blind placebo-controlled randomized clinical trial. One hundred and sixty-four patients are being recruited from December 2021. The trial includes adult patients with incurable solid tumor, CAC, loss of appetite (≥4 score on the 0 to 10 loss of appetite scale of the Edmonton Symptom Assessment System [ESAS]), Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 and a predicted survival >3 months. Causes for exclusion are: highly emetogenic chemotherapy, antipsychotic or appetite stimulant administration, nausea or vomiting score >3 on the 0 to 10 scale of the ESAS, weight gain for a known cause (e.g., oedema) or central nervous system disease. Participants will be 1:1 randomized to the intervention arm (OLZ 5 mg tablet once a night for 28 days) or the control arm (placebo tablet once at night for 28 days). The primary endpoint is the change in the loss of appetite score on the 0 to 10 scale of the ESAS from day 0 to day 7 of treatment. The secondary endpoints include the change in loss of appetite score from day 0 to days 14, 21 and 28 and the change in quality of life, handgrip strength, body mass index, body weight, lean body mass, fatigue and c-reactive protein level from day 0 to day 28.

Clinical trial identification: NCT05243251.

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Optimizing the virtual reality model is a necessity to cope with the nature of the World Wide Web. Virtual reality scenes should load within an acceptable time for the user’s experience and the sense of being immersed in the virtual environment is not to be affected. In this study, we propose to optimize web-based virtual reality models by removing redundant objects within the scenes, while keeping the model readable to webauthors and modelers. The achieved reductions in the model file size and in the model parsing time
were further reduced by introducing structure and appearance tolerance.

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Performance of real-parameter global optimization algorithms is typically evaluated using sets of test problems. We propose a new methodology of extending these benchmarks to obtain a more balanced experimental design. This can be done by selectively removing some of the transformations originally used in the definitions of the test problems such as rotation, scaling, or translation. In this way, we obtain several variants of each problem parametrized by interpretable, high-level characteristics. These binary parameters are used as predictors in a multiple regression model explaining the algorithmic performance. Linear models allow for the attribution of strength and direction of performance changes to particular characteristics of the optimization problems and thus provide insight into the underlying mechanics of the investigated algorithms. The proposed ideas are illustrated with an application example showing the feasibility of the new benchmark. Parametrized benchmarking is a step towards obtaining multi-faceted insight into algorithmic performance and the optimization problems. The overall goal is to systematize a method of matching problems to algorithms and in this way constructively address the limitations imposed by the no free lunch theorem.